# Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms

arXiv: https://arxiv.org/abs/2409.16850 · HTML: https://mlsanity.com/paper/2409.16850/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection](https://mlsanity.com/paper/2103.00879/) (2021)
- [ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments](https://mlsanity.com/paper/2103.05368/) (2021)
- [How to Reduce Change Detection to Semantic Segmentation](https://mlsanity.com/paper/2206.07557/) (2022)
- [The Change You Want to See (Now in 3D)](https://mlsanity.com/paper/2308.10417/) (2023)
- [Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints](https://mlsanity.com/paper/1712.02941/) (2017)
- [City-scale Scene Change Detection using Point Clouds](https://mlsanity.com/paper/2103.14314/) (2021)
- [Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving](https://mlsanity.com/paper/2107.07030/) (2021)

## Built on by (most cited first, 12 of 12)

- [Multi-View Pose-Agnostic Change Localization with Zero Labels](https://mlsanity.com/paper/2412.03911/) (2024)
- [Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis](https://mlsanity.com/paper/2603.22786/) (2026)
- [ViewDelta: Scaling Scene Change Detection through Text-Conditioning](https://mlsanity.com/paper/2412.07612/) (2024)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.07203/) (2026)
- [Environmental Change Detection for Real-World Change Analysis](https://mlsanity.com/paper/2506.11481/) (2025)
- [Leveraging Geometric Priors for Unaligned Scene Change Detection](https://mlsanity.com/paper/2509.11292/) (2025)
- [PlenoCI: Plenoptic CharacterIstics for View Dependence Aware Change Classification](https://mlsanity.com/paper/2609.28930/) (2026)
- [SCD4VPR: Multi-modal Scene Change Detection for Long-term Visual Place Recognition Database Update](https://mlsanity.com/paper/2604.11402/) (2026)
- [TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection](https://mlsanity.com/paper/2605.20822/) (2026)
- [ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring](https://mlsanity.com/paper/2610.01283/) (2026)

## Abstract (arXiv)

We present a novel method for scene change detection that leverages the robust feature extraction capabilities of a visual foundational model, DINOv2, and integrates full-image cross-attention to address key challenges such as varying lighting, seasonal variations, and viewpoint differences. In order to effectively learn correspondences and mis-correspondences between an image pair for the change detection task, we propose to a) ``freeze'' the backbone in order to retain the generality of dense foundation features, and b) employ ``full-image'' cross-attention to better tackle the viewpoint variations between the image pair. We evaluate our approach on two benchmark datasets, VL-CMU-CD and PSCD, along with their viewpoint-varied versions. Our experiments demonstrate significant improvements in F1-score, particularly in scenarios involving geometric changes between image pairs. The results indicate our method's superior generalization capabilities over existing state-of-the-art approaches, showing robustness against photometric and geometric variations as well as better overall generalization when fine-tuned to adapt to new environments. Detailed ablation studies further validate the contributions of each component in our architecture. Our source code is available at: https://github.com/ChadLin9596/Robust-Scene-Change-Detection.